Submission¶

Put the ipynb file and html file in the github branch you created in the last assignment and submit the link to the commit in brightspace

In [2]:
from plotly.offline import init_notebook_mode
import plotly.io as pio
import plotly.express as px

init_notebook_mode(connected=True)
pio.renderers.default = "plotly_mimetype+notebook"
In [3]:
#load data
df = px.data.gapminder()
df.head()
Out[3]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4

Question 1:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [4]:
# Filter the DataFrame to select data for the year 2007
df = px.data.gapminder()
df_2007 = df[df['year']==2007]

# Group the filtered data by continent and calculate the sum of numeric columns
df_2007_new = df_2007.groupby('continent') .agg({'pop': 'sum'}).reset_index()

# Create a bar chart using Plotly Express
df_2007_new_sorted = df_2007_new.sort_values(by='pop', ascending=False)

def format_population(pop):
    if pop < 1e9:
        return f'{pop/1e6:.0f}M'
    else:
        return f'{pop/1e9:.1f}B'

df_2007_new_sorted['pop_text'] = df_2007_new_sorted['pop'].apply(format_population)

fig = px.bar(df_2007_new_sorted, x='pop', y='continent', color='continent',
             color_discrete_map={'Asia': 'blue', 'Africa': 'green', 'Europe': 'red', 'North America': 'purple', 'South America': 'orange', 'Oceania': 'brown'},
             labels={'continent': 'Continent', 'pop': 'Population'},
             text='pop_text')  

# Customize the layout of the chart: hide the legend
fig.update_layout(showlegend=False)

# Update the layout for the y-axis to order categories by total population in ascending order
fig.update_xaxes(categoryorder='total ascending')

# Customize the text labels on the bars: format with two decimal places and position them outside the bars
fig.update_traces(textposition='outside')

# Display the resulting chart
fig.show()

Question 2:¶

Sort the order of the continent for the visualisation

Hint: Use axis layout setting

In [10]:
# Below are the parts of the code in question 1 that decide the order of the continents:

# df_2007_new_sorted = df_2007_new.sort_values(by='pop', ascending=False)

# fig.update_xaxes(categoryorder='total ascending')

Question 3:¶

Add text to each bar that represents the population

In [11]:
# Below are the parts of the code in question 1 that create the text on each bar:

# fig = px.bar(df_2007_new_sorted, x='pop', y='continent', color='continent',
             # color_discrete_map={'Asia': 'blue', 'Africa': 'green', 'Europe': 'red', 'North America': 'purple', 'South America': 'orange', 'Oceania': 'brown'},
             # labels={'continent': 'Continent', 'pop': 'Population'},
             # text='pop_text')  
            
# fig.update_traces(textposition='outside')
            

Question 4:¶

Thus far we looked at data from one year (2007). Lets create an animation to see the population growth of the continents through the years

In [5]:
# Load the Gapminder dataset
df = px.data.gapminder()

# Create a bar chart animation using Plotly Express
fig = px.bar(
    df,
    x="pop",
    y="continent",
    color="continent",
    animation_frame="year",  # Use 'year' as the animation frame
    color_discrete_map={
        'Asia': 'blue',
        'Africa': 'green',
        'Europe': 'red',
        'North America': 'purple',
        'South America': 'orange',
        'Oceania': 'brown',
    },
    labels={'continent': 'Continent', 'pop': 'Population'},
    title='Population Growth by Continent Over Time'
)

# Remove the vertical grid lines
fig.update_traces(selector=dict(type='bar'), marker=dict(line=dict(width=0)))

# Customize the layout of the chart
fig.update_layout(showlegend=False)
fig.update_xaxes(categoryorder='total ascending')

# Display the animated chart
fig.show()

Question 5:¶

Instead of the continents, lets look at individual countries. Create an animation that shows the population growth of the countries through the years

In [6]:
# Load the Gapminder dataset
df = px.data.gapminder()

# Create a bar chart animation using Plotly Express
fig = px.bar(
    df,
    x="pop",
    y="country",  # Use 'country' instead of 'continent'
    color="country",  # Color by country
    animation_frame="year",  # Use 'year' as the animation frame
    labels={'country': 'Country', 'pop': 'Population'},
    title='Population Growth by Country Over Time'
)

# Customize the layout of the chart
fig.update_layout(showlegend=False)
fig.update_xaxes(categoryorder='total ascending')

# Display the animated chart
fig.show()

Question 6:¶

Clean up the country animation. Set the height size of the figure to 1000 to have a better view of the animation

In [7]:
# Load the Gapminder dataset
df = px.data.gapminder()

# Create a bar chart animation using Plotly Express
fig = px.bar(
    df,
    x="pop",
    y="country",  # Use 'country' instead of 'continent'
    color="country",  # Color by country
    animation_frame="year",  # Use 'year' as the animation frame
    labels={'country': 'Country', 'pop': 'Population'},
    title='Population Growth by Country Over Time'
)

# Customize the layout of the chart
fig.update_layout(
    showlegend=False,
    height=1000,  # Set the height of the figure to 1000
)

# Display the animated chart
fig.show()

Question 7:¶

Show only the top 10 countries in the animation

Hint: Use the axis limit to set this.

In [8]:
# Load the Gapminder dataset
df = px.data.gapminder()

# Get the top 10 countries by population for the initial frame (year 1952)
top_10_countries = df[df['year'] == 1952].nlargest(10, 'pop')['country']

# Create a filtered DataFrame containing only the top 10 countries
df_filtered = df[df['country'].isin(top_10_countries)]

# Create a bar chart animation using Plotly Express
fig = px.bar(
    df_filtered,  # Use the filtered DataFrame with only the top 10 countries
    x="pop",
    y="country",  # Use 'country' instead of 'continent'
    color="country",  # Color by country
    animation_frame="year",  # Use 'year' as the animation frame
    labels={'country': 'Country', 'pop': 'Population'},
    title='Top 10 Population Growth by Country Over Time'
)

# Customize the layout of the chart
fig.update_layout(
    showlegend=False,
    height=1000,  # Set the height of the figure to 1000
)

# Set x-axis limits to show the entire range of population values
fig.update_xaxes(range=[0, df['pop'].max()])
fig.update_yaxes(categoryorder='total ascending')

# Display the animated chart
fig.show()
In [ ]: